docs: def shifted_mean_gauss(image, offset = None, sigma = 5, voxelspacing = None, mask = slice(None)):
      r"""
      The approximate mean over a small region at an offset from each voxel.
      
      Functions like `local_mean_gauss`, but instead of computing the average over a small
      patch around the current voxel, the region is centered at an offset away. Can be used
      to use a distant regions average as feature for a voxel.
      
      Parameters
      ----------
      image : array_like or list/tuple of array_like 
          A single image or a list/tuple of images (for multi-spectral case).
      offset : sequence of ints
          At this offset in voxels of the current position the region is placed.
      sigma : number or sequence of numbers
          Standard deviation for Gaussian kernel. The standard deviations of the
          Gaussian filter are given for each axis as a sequence, or as a single number,
          in which case it is equal for all axes. Note that the voxel spacing of the image
          is taken into account, the given values are treated as mm.
      voxelspacing : sequence of floats
          The side-length of each voxel.
      mask : array_like
          A binary mask for the image. 
      
      Returns
      -------
      shifted_mean_gauss : ndarray
          The weighted mean intensities over a region at offset away from each voxel.
      
      See also
      --------
      local_mean_gauss
      
      """
      return _extract_feature(_extract_shifted_mean_gauss, image, mask, offset = offset, sigma = sigma, voxelspacing = voxelspacing)